Systems and Methods for Generating a Plain English Interpretation of a Legal Clause

1. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: identify one or more legal clause interpretations in a plurality of attorney communications; train a neural network (NN) based on the identified one or more legal clause interpretations; provide a first legal clause to the trained NN and a probability model; generate, via the trained NN, a first non-legalese interpretation based on the first legal clause; provide the first non-legalese interpretation to a probability model; generate, using the probability model, a probability score based on a degree to which the first legal clause matches the non-legalese interpretation in meaning; determine whether the probability score exceeds a predetermined threshold; when the probability score does not exceed the predetermined threshold, instruct the NN to generate a second non-legalese interpretation based on the first legal clause; and when the probability score exceeds the predetermined threshold, output the first non-legalese interpretation.

2. The system of claim 1 , wherein the probability model is a convolutional neural network (CNN) and the NN is either a CNN or a recurrent neural network (RNN).

3. The system of claim 2 , wherein the plurality of attorney communications comprises a plurality of email communications.

4. The system of claim 3 , wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting a redline change in a document attached to one of the plurality of email communications and identifying a paragraph associated with the redline change as a first legal clause interpretation of the one or more legal clause interpretations.

5. The system of claim 3 , wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting an addition in a document attached to one of the plurality of email communications and identifying a paragraph associated with the addition as a first legal clause interpretation of the one or more legal clause interpretations.

6. The system of claim 3 , wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting a comment in a document attached to one of the plurality of email communications and identifying text within the comment as a first legal clause interpretation of the one or more legal clause interpretations.

7. The system of claim 1 , wherein the instructions, when executed by the one or more processors, are further configured to cause the system to: receive, from a user device, reinforcement feedback based on the first non-legalese interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.

8. The system of claim 6 , wherein the output of the first non-legalese interpretation is in a chat program accessible by the user device and a reinforcement feedback is provided from the user device via the chat program.

9. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: provide a first legal clause to a trained neural network (NN); generate, via the trained NN, a first non-legalese interpretation based on the first legal clause; receive, from a user device, reinforcement feedback based on the first non-legalese interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.

10. The system of claim 9 , wherein the NN is either a convolutional neural network (CNN) or a recurrent neural network (RNN).

11. The system of claim 10 , wherein the instructions, when executed by the one or more processors, are further configured to cause the system to: identify one or more legal clause interpretations in a plurality of attorney communications; train the neural network based on the identified one or more legal clause interpretations, and wherein the plurality of attorney communications comprises a plurality of email communications.

12. The system of claim 11 , wherein identifying the one or more legal clause interpretation request in the plurality of attorney communications comprises detecting a redline change in a document attached to one of the plurality of email communications and identifying a paragraph associated with the redline change as a first legal clause interpretation of the one or more legal clause interpretations.

13. The system of claim 11 , wherein identifying the one or more legal clause interpretation request in the plurality of attorney communications comprises detecting an addition in a document attached to one of the plurality of email communications and identifying a paragraph associated with the addition as a first legal clause interpretation of the one or more legal clause interpretations.

14. The system of claim 11 , wherein identifying the one or more legal clause interpretation request in the plurality of attorney communications comprises detecting a comment in a document attached to one of the plurality of email communications and identifying text within the comment as a first legal clause interpretation of the one or more legal clause interpretations.

15. The system of claim 9 , wherein the output of the first non-legalese interpretation is in a chat program accessible by the user device and the reinforcement feedback is provided from the user device via the chat program.

16. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: provide a first legal clause to a trained neural network (NN) and a probability model; generate, via the trained NN, a first non-legalese interpretation based on the first legal clause; provide the first non-legalese interpretation to a probability model; generate, using the probability model, a probability score based on a degree to which the legal clause matches the non-legalese interpretation in meaning; determine whether the probability score exceeds a predetermined threshold; when the probability score does not exceed the predetermined threshold, instruct the NN to generate a second non-legalese interpretation based on the first legal clause; and when the probability score exceeds the predetermined threshold, output the first non-legalese interpretation.

17. The system of claim 16 , wherein the probability model is a convolutional neural network (CNN) and the neural network is at either a CNN or a recurrent neural network (RNN).

18. The system of claim 16 , wherein the instructions, when executed by the one or more processors, are further configured to cause the system to: receive, from a user device, reinforcement feedback based on the first non-legalese interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.

19. The system of claim 18 , wherein the output of the first non-legalese interpretation is in a chat program accessible by the user device and the reinforcement feedback is provided from the user device via the chat program.

20. The system of claim 16 , wherein the first non-legalese interpretation comprises a first plain English interpretation.